Results 91 to 100 of about 101,252 (223)
Multidirectional Subspace Expansion for One-Parameter and Multiparameter Tikhonov Regularization [PDF]
, 2016 Tikhonov regularization is a popular method to approximate solutions of linear discrete ill-posed problems when the observed or measured data is contaminated by noise.
Michiel E. Hochstenbach +5 more
core +4 more sources
MSI: A Mahalanobis‐Based Molecular Similarity Index for High‐Dimensional Embeddings
Molecular Informatics, Volume 45, Issue 9, September 2026.MSI pairs continuous Mol2Vec embeddings with a covariance‐aware Mahalanobis metric (dM, θM) to overcome the ranking ties and bit‐density bias of Tanimoto fingerprints. Across five reference compounds, MSI resolves more distinct neighbors than ECFP4 + Tanimoto and its geometry tracks HOMO–LUMO gaps in QM9, despite no electronic training.
Roberto Bernal‐Jaquez +4 more
wiley +1 more source
Convergence rates of general regularization methods for statistical inverse problems and applications [PDF]
During the past the convergence analysis for linear statistical inverse problems has mainly focused on spectral cut-off and Tikhonov type estimators. Spectral cut-off estimators achieve minimax rates for a broad range of smoothness classes and operators,
Bissantz, Nicolai +3 more
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Modulus-based iterative methods for constrained Tikhonov regularization [PDF]
, 2017 Tikhonov regularization is one of the most popular methods for the solution of linear discrete ill-posed problems. In many applications the desired solution is known to lie in the nonnegative cone.
Bai Z. -Z. +11 more
core +1 more source
Shock and Vibration, 2019 In the research of dynamic load identification, the method of obtaining kernel function matrix is usually rather cumbersome. To solve this problem, an explicit dynamic load identification algorithm based on the Wilson-θ (DLIAEW) method is proposed to ...
Yuchuan Fan, Chunyu Zhao, Hongye Yu
doaj +1 more source
Sampling Noise and Optimized Measurement Distribution in Imaginary‐Time Quantum Dynamics Simulations
Advanced Quantum Technologies, Volume 9, Issue 9, September 2026.Finite‐shot sampling noise fundamentally limits the accuracy and efficiency of variational quantum algorithms on near‐term quantum devices. Using noisy simulations of variational quantum imaginary‐time evolution as a representative example, an optimized shot‐allocation strategy with a minimum‐shot constraint is shown to improve convergence, enhance ...
Feng Zhang +5 more
wiley +1 more source
The Cross‐Kernel Margin: A Robustness Measure for Quantum Kernel Methods
Advanced Quantum Technologies, Volume 9, Issue 9, September 2026.The cross‐kernel margin is introduced as a robustness measure for Quantum Kernel‐Assisted Support Vector Machines. This metric evaluates a classifier learned from a perturbed kernel within the ideal, unperturbed kernel geometry. Derived stability bounds quantify the corresponding inverse squared‐margin deviation and are numerically tested under local ...
S. Govender, I. Sinayskiy
wiley +1 more source
IEEE Access, 2018 Particle size distribution (PSD) measurement based on the static light scattering method has been widely used in the environmental field and combustion diagnostics, such as PM2.5 measurement and combustion process monitoring.
Ming Kong +3 more
doaj +1 more source
The Ensemble Kalman Inversion Race
Journal of Advances in Modeling Earth Systems, Volume 18, Issue 9, September 2026.Abstract Ensemble Kalman methods were initially developed to solve nonlinear data assimilation problems in oceanography but are now popular in applications far beyond their original use cases. Of particular interest is climate model calibration.
Rebecca Gjini +3 more
wiley +1 more source
IDENTIFICATION AND ESTIMATION OF NONPARAMETRIC STRUCTURAL [PDF]
This paper concerns a new statistical approach to instrumental variables (IV) method for nonparametric structural models with additive errors. A general identifying condition of the model is proposed, based on richness of the space generated by marginal ...
Woocheol Kim
core